Evaluating the revenue and taxation implications of cannabis legalization in South Africa: Insights from Canada and the United States
Bibliographic record
Abstract
Purpose: This study aims to evaluate the impact of cannabis legalization on revenue mobilization and taxation in South Africa by analyzing the experiences of jurisdictions that have legalized cannabis. Methodology: A qualitative approach was employed, including a literature review and trend analysis. Data from Canada, California, Colorado, and Washington State, where cannabis has been legalized, were analyzed for 2018-2021. Results: The findings demonstrate that cannabis legalization significantly impacts revenue collection and taxation. However, optimal pricing and taxation policies are crucial to capture the illicit market, minimize negative externalities, and ensure industry growth. Legacy growers and previously disadvantaged individuals should be integrated into the legal market. Theoretical Contribution: The study contributes to the theoretical framework of Pigouvian taxation by examining its applicability to the cannabis industry and the challenges posed by illicit markets and product substitutability with alcohol and tobacco. Practical Implications: The study provides recommendations for South African policymakers on taxation policies, market regulation, and inclusive strategies to ensure a successful and sustainable cannabis industry while maximizing revenue mobilization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".